Salīdzināt metodes

	Grafu neironu tīkls	Hierarhiskā klasterizācija
Nozare	Dziļā mācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017	1963
Autors	Kipf, T.N. & Welling, M.	Ward, J. H.
Tips	Deep learning on graph-structured data	Unsupervised clustering (agglomerative)
Pirmavots	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Citi nosaukumi	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Saistītās	4	4
Kopsavilkums	A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.